Dissertations / Theses on the topic 'Photoplethysmographic signal'
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Li, Kejia. "Wireless reflectance pulse oximeter design and photoplethysmographic signal processing." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4143.
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Odinsdottir, Gudny Björk, and Jesper Larsson. "Deep Learning Approach for Extracting Heart Rate Variability from a Photoplethysmographic Signal." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-21368.
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Photoplethysmography (PPG) is a method to detect blood volume changes in every heartbeat. The peaks in the PPG signal corresponds to the electrical impulses sent by the heart. The duration between each heartbeat varies, and these variances are better known as heart rate variability (HRV). Thus, finding peaks correctly from PPG signals provides the opportunity to measure an accurate HRV. Additional research indicates that deep learning approaches can extract HRV from a PPG signal with significantly greater accuracy compared to other traditional methods. In this study, deep learning classifiers were built to detect peaks in a noise-contaminated PPG signal and to recognize the performed activity during the data recording. The dataset used in this study is provided by the PhysioBank database consisting of synchronized PPG-, acceleration- and gyro data. The models investigated in this study were limited toa one-layer LSTM network with six varying numbers of neurons and four different window sizes. The most accurate model for the peak classification was the model consisting of 256 neurons and a window size of 15 time steps, with a Matthews correlation coefficient (MCC) of 0.74. The model consisted of64 neurons and a window duration of 1.25 seconds resulted in the most accurate activity classification, with an MCC score of 0.63. Concludingly, more optimization of a deep learning approach could lead to promising accuracy on peak detection and thus an accurate measurement of HRV. The probable cause for the low accuracy of the activity classification problem is the limited data used in this study.
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Johnston, William S. "Development of a signal processing library for extraction of SpO2, HR, HRV, and RR from photoplethysmographic waveforms." Worcester, Mass. Worcester Polytechnic Institute, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-073106-130906/.
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Thesis (M.S.)--Worcester Polytechnic Institute.Keywords: wearable medical sensors; arterial oxygen saturation; software development; embedded systems; heart rate; respiration rate; heart rate variability; pulse oximetry; digital signal processing Includes bibliographical references (leaves 125-133).
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Alomari, Abdul-Hakeem Hussein Electrical Engineering & Telecommunications Faculty of Engineering UNSW. "Spectral analysis of arterial blood prssure and stroke volume variability: the role of Calcium channel blockers and sensitizers." Publisher:University of New South Wales. Electrical Engineering & Telecommunications, 2008. http://handle.unsw.edu.au/1959.4/43923.
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In this thesis, we included results from two studies. The first one considered the effects of the blood volume changes, during blood donation, on the heart rate variability (HRV) measured, non-invasively, form electrocardiographic (ECG) and photoplethysmographic (PPG) signals. Our results showed that, during blood donation, there were no significant changes in the pulsatile area of PPG signal, while heart rate increased. No significant changes were noticed in HRV extracted from both signals. Error analysis between the HRV extracted from ECG and peak interval variability (PIV) suggested that the error during blood donation was increased which means that the use of PIV extracted from PPG signal, used as a replacement diagnostic tool in clinical applications, needs further investigations and should be carefully studied in non-stationary cardiovascular situations such as blood donation. The imbalance between the two branches of the autonomic nervous system, sympathetic and parasympathetic, vagal, may result in a harmful activation of myocardial tissues which cause arrhythmias and sudden cardiac death. Although the study of the sympathovagal balance have been attracting many researchers, further studies are needed to elucidate the effects of many kinds of drugs on the autonomic modulation of the cardiac muscle, specifically, the cells of sinoatrial (SA) node. The aim of the second part of this thesis was to assess the effects of calcium channel blocker (Verapamil), calcium channel sensitizer (Levosimendan), calcium chloride (CaCl2), the combinations of verapamil/ CaCl2, levosimendan/ CaCl2, and noradrenaline infusion on beat-to-beat cardiovascular variability represented, in this research, by systolic blood pressure variability (SBPV), and stroke volume variability (SVV) signals. We used Fat Fourier Transform (FFT) to evaluate the power spectral density of the fluctuations in both signals to evaluate the effects of short-term treatments with those drugs on the sympathovagal balance in normal rats. Then, we compared the spectra obtained from SBPV and SVV to decide which of these fluctuations along with corresponding spectrum was more able to provide a clear feedback about the autonomic nervous system. Our data suggests that there were a significant correlations between low- (LF), mid- (MF), and high-frequency (HF) spectra obtained from SBPV and SVV except between the HF spectra estimated from after the infusion of levosimendan where a poor correlation (r = 0.530, p = 0.281) was noticed. This that both HF components obtained provide different information regarding the autonomic nervous system modulation of the SA node cells, while the results obtained from the rest of experiments showed that both signals provide same information about the modulation of sympathetic and parasympathetic tone due to all stages of different drugs infusion studied in this thesis. Besides that, we found that both spectra may be used to track the fluctuations in the cardiac output as a result of the drugs infusion.
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Cherif, Safa. "Effective signal processing methods for robust respiratory rate estimation from photoplethysmography signal." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0094/document.
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Le photopléthysmogramme (PPG) est un signal optique acquis à partir de l’oxymètre de pouls, dont l’usage principal consiste à mesurer la saturation en oxygène. Avec le développement des technologies portables, il est devenu la technique de base pour la surveillance de l’activité cardio-respiratoire des patients et la détection des anomalies. En dépit de sa simplicité d'utilisation, le déploiement de cette technique reste encore limité pour deux principales raisons : 1. L’extrême sensibilité du signal aux distorsions. 2. La non-reproductibilité entre les sujets et pour les mêmes sujets, en raison de l'âge et des conditions de santé. L’objectif de cette thèse est le développement de méthodes robustes et universelles afin d’avoir une estimation précise de la fréquence respiratoire (FR) indépendamment de la variabilité intra et interindividuelle du PPG. Plusieurs contributions originales en traitement statistiques du signal PPG sont proposées. En premier lieu, une méthode adaptative de détection des artefacts basée sur la comparaison de modèle a été développée. Des tests par la technique Random Distortion Testing sont introduits pour détecter les pulses de PPG avec des artefacts. En deuxième lieu, une analyse de plusieurs méthodes spectrales d’estimation de la FR est proposée. Afin de mettre en évidence la robustesse des méthodes proposées face à la variabilité du signal, plusieurs tests ont été effectués sur deux bases de données avec de différentes tranches d'âge et des différents modes respiratoires. En troisième lieu, un indice de qualité respiratoire spectral (SRQI) est conçu dans le but de communiquer au clinicien que les valeurs fiables de la FR avec un certain degré de confianceOne promising area of research in clinical routine involves using photoplethysmography (PPG) for monitoring respiratory activities. PPG is an optical signal acquired from oximeters, whose principal use consists in measuring oxygen saturation. Despite its simplicity of use, the deployment of this technique is still limited because of the signal sensitivity to distortions and the non-reproducibility between subjects, but also for the same subject, due to age and health conditions. The main aim of this work is to develop robust and universal methods for estimating accurate respiratory rate regardless of the intra- and inter-individual variability that affects PPG features. For this purpose, firstly, an adaptive artefact detection method based on template matching and decision by Random Distortion Testing is introduced for detecting PPG pulses with artefacts. Secondly, an analysis of several spectral methods for Respiratory Rate (RR) estimation on two different databases, with different age ranges and different respiratory modes, is proposed. Thirdly, a Spectral Respiratory Quality Index (SRQI) is attributed to respiratory rate estimates, in order that the clinician may select only RR values with a large confidence scale. Promising results are found for two different databases
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Schäck, Tim [Verfasser], Abdelhak M. [Akademischer Betreuer] Zoubir, D. Robert [Akademischer Betreuer] Iskander, and Michael [Akademischer Betreuer] Muma. "Photoplethysmography-Based Biomedical Signal Processing / Tim Schäck ; Abdelhak M. Zoubir, D. Robert Iskander, Michael Muma." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2019. http://d-nb.info/1176702009/34.
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Patancheru, Govardhan Reddy. "Wearable Heart Rate Measuring Unit." Thesis, Mittuniversitetet, Avdelningen för elektronikkonstruktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-23351.
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Despite having the numerous evolved heart rate measuring devices and progress in their development over the years, there always remain the challenges of modern signal processing implementation by a comparatively small size wearable device. This thesis paper presents a wearable reflectance photoplethysmography (PPG) sensor system for measuring the heart rate of a user both in steady and moving states. The size and, power consumption of the device are considered while developing, to ensure an easy deployment of the unit at the measuring site and the ability to power the entire unit with a battery .The selection of both the electronic circuits and signal processing techniques is based on their sensitivity to PPG signals, robustness against noise inducing artifacts and miniaturization of the entire measuring unit. The entire signal chain operates in the discrete-time, which allows the entire signal processing to be implemented in firmware on an embedded microprocessor. The PPG sensor system is implemented on a single PCB that consumes around 7.5mW of power. Benchmarking tests with standard heart rate measuring devices reveal that the developed measurement unit (combination of the PPG sensor system, and inertial measurement unit (IMU) developed in-house at Acreo Swedish ICT, and a battery) is comparable to the devices in detecting heart rate even in motion artifacts environment. This thesis work is carried out in Acreo Swedish ICT, Gothenburg, Sweden in collaboration with MidSweden University, Sundsvall, Department of Electronics Design. This report can be used as ground work for future development of wearable heart rate measuring units at Acreo Swedish ICT.
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Alghoul, Karim. "Heart Rate Variability Extraction from Video Signals." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33003.
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Heart Rate Variability (HRV) analysis has been garnering attention from researchers due to its wide range of applications. Medical researchers have always been interested in Heart Rate (HR) and HRV analysis, but nowadays, investigators from variety of other fields are also probing the subject. For instance, variation in HR and HRV is connected to emotional arousal. Therefore, knowledge from the fields of affective computing and psychology, can be employed to devise machines that understand the emotional states of humans. Recent advancements in non-contact HR and HRV measurement techniques will likely further boost interest in emotional estimation through . Such measurement methods involve the extraction of the photoplethysmography (PPG) signal from the human's face through a camera. The latest approaches apply Independent Component Analysis (ICA) on the color channels of video recordings to extract a PPG signal. Other investigated methods rely on Eulerian Video Magnification (EVM) to detect subtle changes in skin color associated with PPG.
The effectiveness of the EVM in HR estimation has well been established. However, to the best of our knowledge, EVM has not been successfully employed to extract HRV feature from a video of a human face. In contrast, ICA based methods have been successfully used for HRV analysis. As we demonstrate in this thesis, these two approaches for HRV feature extraction are highly sensitive to noise. Hence, when we evaluated them in indoor settings, we obtained mean absolute error in the range of 0.012 and 28.4.
Therefore, in this thesis, we present two approaches to minimize the error rate when estimating physiological measurements from recorded facial videos using a standard camera. In our first approach which is based on the EVM method, we succeeded in extracting HRV measurements but we could not get rid of high frequency noise, which resulted in a high error percentage for the result of the High frequency (HF) component. Our second proposed approach solved this issue by applying ICA on the red, green and blue (RGB) colors channels and we were able to achieve lower error rates and less noisy signal as compared to previous related works. This was done by using a Buterworth filter with the subject's specific HR range as its Cut-Off.
The methods were tested with 12 subjects from the DISCOVER lab at the University of Ottawa, using artificial lights as the only source of illumination. This made it a challenge for us because artificial light produces HF signals which can interfere with the PPG signal. The final results show that our proposed ICA based method has a mean absolute error (MAE) of 0.006, 0.005, 0.34, 0.57 and 0.419 for the mean HR, mean RR, LF, HF and LF/HF respectively. This approach also shows that these physiological parameters are highly correlated with the results taken from the electrocardiography (ECG).
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Chang, Serene Hsi-Lin. "Clinical evaluation of a new optical fibre method of measuring oxygen saturation using photoplethysmograph signals reflected from internal tissues." Thesis, Queen Mary, University of London, 2013. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8719.
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Traditional methods of measuring oxygen saturation, e.g. pulse oximetry, depend on an adequate peripheral circulation and have a 20–30 second lag time before readings are obtained. This was a series of evaluations of novel optical probes, designed to measure oxygen saturation using fibreoptic technology directly from internal organs including the brain, oesophagus and organs with splanchnic circulations. A series of pilot studies were proposed and research ethics approval obtained to carry out studies in humans, under general anaesthesia, using these probes. Innovative reflectance probes were designed specifically for each of the four applications, so as to obtain potentially useful signals needed for signal processing, analysis and evaluation. Signals were successfully obtained from the brain, oesophagus and splanchnic region in almost all of the patients recruited. Good quality photoplethysmograph signals were recorded and these were translated into clinically meaningful values of oxygen saturation comparable to traditional methods of pulse oximetry. Overall, the signals were prone to movement artefacts as well as occasional interference from surgical diathermy and other sources. Nonetheless, the probes could prove to be a useful alternative to conventional external transmittance pulse oximetry methods as well as providing useful information regarding regional perfusion and oxygenation. The success of these pilot studies will form the basis of more research in the area and further development of such probes on the medical engineering front.
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Uggla, Lingvall Kristoffer. "Remote heart rate estimation by evaluating measurements from multiple signals." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210303.
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Heart rate can say a lot about a person's health. While most conventional methods for heart rate measurement require contact with the subject, these are not always applicable. In this thesis, a non-invasive method for pulse detection is implemented and analyzed. Different signals from the color of the forehead—including the green channel, the hue channel and different ICA and PCA components—are inspected, and their resulted heart rates are weighted together according to the significance of their FFT peaks. The system is tested on videos with different difficulties regarding the amount of movement and setting of the scene. The results show that the approach of weighting measurements from different signals together has great potential. The system in this thesis, however, does not perform very well on videos with a lot of movement because of motion noise. Though, with better, less noisy signals, good results can be expected.En människas puls säger en hel del om dennes hälsa. För att mäta pulsenanvänds vanligtvis metoder som vidrör människan, vilket iblandär en nackdel. I det här examensarbetet tas en metod för pulsmätningpå avstånd fram, som endast använder klipp från en vanlig videokamera. Färgen i pannan mäts och utifrån den genereras flera signalersom analyseras, vilket resulterar i olika mätvärden för pulsen. Genomatt värdera dessa mätvärden med avseende på hur tydliga signalernaär, beräknas ett viktat medelvärde som ett slutgiltigt estimat på medelpulsen. Metoden testas på videoklipp med varierande svårighetsgrad,beroende på hur mycket rörelser som förekommer och på vilketavstånd från kameran försökspersonen står. Resultaten visar att metodenhar mycket god potential och att man kan man förvänta sig finaresultat med bättre, mindre brusiga signaler.
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Knörzer, Stephan Hartmut [Verfasser], Gottfried [Akademischer Betreuer] Schmalz, and Peter [Akademischer Betreuer] Proff. "In vitro-Untersuchungen zum Einfluss nicht-pulpaler Signalquellen und unterschiedlicher Blutflussbedingungen auf an Zähnen gemessene Photoplethysmographie-Signale bei physiologischer Durchblutungsrate / Stephan Hartmut Knörzer ; Gottfried Schmalz, Peter Proff." Regensburg : Universitätsbibliothek Regensburg, 2016. http://d-nb.info/1113875550/34.
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Vařečka, Martin. "Stanovení krevního tlaku pomocí chytrého telefonu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-378143.
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Blood pressure is one of the basic indicators of the health state of the cardiovascular system. High blood pressure is the main risk factor of ischemic heart disease, atherosclerosis and stroke. Therefore, it is important to monitor long-term changes in blood pressure and respond in time to these changes. Blood pressure meters are not standard household equipment, while a well-equipped smartphone is. Smartphones contain a large number of sensors capable of measuring biomedical signals. This thesis focuses on creating an application capable of determining blood pressure using data obtained from these sensors.
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Поврозник, Наталія Іванівна, and Natalia Povroznyk. "Імітаційна модель пульсового сигналу для тестування алгоритмів опрацювання фотоплетизмографічних систем." Thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2017. http://elartu.tntu.edu.ua/handle/123456789/18874.
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У дипломній роботі розроблено комп’ютерну імітаційну модель пульсового сигналу у вигляді періодично подовжених сум двох функцій нормального розподілу із урахуванням випадковості, періодичності та зміни фази коливання. Імітаційна модель дає можливість за відомими медичними параметрами моделювати сигнали патологій і норм для задачі для задачі верифікації методів опрацювання сигналів в фотоплетизмографічних системах.
Розроблено програмне забезпечення в середовищі Matlab з метою автоматизації процесу імітування пульсових сигналів.In the thesis work developed a computer simulation model of pulse signal as periodically extended amounts of two functions of normal distribution, taking into account the randomness, periodical and phase change fluctuations. A simulation model enables the known medical model parameters and signals pathologies and norms rules for task verification methods processing in fotopletyzmohrafichnyh signal processing systems. The software is developed in Matlab environment to automate the process of imitation pulse signals.
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Benetti, Tiago. "Estimativa robusta da frequ?ncia card?aca a partir de sinais de fotopletismografia de pulso." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2018. http://tede2.pucrs.br/tede2/handle/tede/8337.
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Heart rate monitoring using Photoplethysmography (PPG) signals acquired from the individuals pulse has become popular due to emergence of numerous low cost wearable devices. However, monitoring during physical activities has obstacles because of the influence of motion artifacts in PPG signals. The objective of this work is to introduce a new algorithm capable of removing motion artifacts and estimating heart rate from pulse PPG signals. Normalized Least Mean Square (NLMS) and Recursive Least Squares (RLS) algorithms are proposed for an adaptive filtering structure that uses acceleration signals as reference to remove motion artifacts. The algorithm uses the Periodogram of the filtered signals to extract their heart rates, which will be used together with a PPG Signal Quality Index to feed the input of a Kalman Filter. Specific heuristics and the Quality Index collaborate so that the Kalman filter provides a heart rate estimate with high accuracy and robustness to measurement uncertainties. The algorithm was validated from the heart rate obtained from Electrocardiography signals and the proposed method with the RLS algorithm presented the best results with an absolute mean error of 1.54 beats per minute (bpm) and standard deviation of 0.62 bpm, recorded for 12 individuals performing a running activity on a treadmill with varying speeds. The results make the performance of the algorithm comparable and even better than several recently developed methods in this field. In addition, the algorithm presented a low computational cost and suitable to the time interval in which the heart rate estimate is performed. Thus, it is expected that this algorithm will improve the obtaining of heart rate in currently available wearable devices.
O monitoramento da frequ?ncia card?aca utilizando sinais de Fotopletismografia ou PPG (do ingl?s, Photopletismography) adquiridos do pulso de indiv?duos tem se popularizado devido ao surgimento de in?meros dispositivos wearable de baixo custo. No entanto, o monitoramento durante atividades f?sicas tem dificuldades em raz?o da influ?ncia de artefatos de movimento nos sinais de PPG. O objetivo deste trabalho ? introduzir um novo algoritmo capaz de remover artefatos de movimento e estimar a frequ?ncia card?aca de sinais de PPG de pulso. Os algoritmos do M?nimo Quadrado M?dio Normalizado ou NLMS (do ingl?s, Normalized Least Mean Square) e de M?nimos Quadrados Recursivos ou RLS (do ingl?s, Recursive Least Squares) s?o propostos para uma estrutura de filtragem adaptativa que utiliza sinais de acelera??o como refer?ncia para remover os artefatos de movimento. O algoritmo utiliza o Periodograma dos sinais filtrados para extrair suas frequ?ncias card?acas, que ser?o utilizadas juntamente com um ?ndice de Qualidade do Sinal de PPG para alimentar a entrada de um Filtro de Kalman. Heur?sticas espec?ficas e o ?ndice de Qualidade colaboram para que filtro de Kalman forne?a uma estimativa da frequ?ncia card?aca com alta acur?cia e robustez a incertezas de medi??o. O algoritmo foi validado a partir da frequ?ncia card?aca obtida de sinais de Eletrocardiografia e o m?todo proposto com o algoritmo RLS apresentou os melhores resultados com um erro m?dio absoluto de 1,54 batimentos por minuto (bpm) e desvio padr?o de 0,62 bpm, registrados para 12 indiv?duos realizando uma atividade de corrida em uma esteira com velocidades variadas. Os resultados tornam o desempenho do algoritmo compar?vel e at? mesmo melhor que v?rios m?todos desenvolvidos recentemente neste campo. Al?m disso, o algoritmo apresentou um custo computacional baixo e adequado ao intervalo de tempo em que a estimativa da frequ?ncia card?aca ? realizada. Dessa forma, espera-se que este algoritmo melhore a obten??o da frequ?ncia card?aca em dispositivos wearable atualmente dispon?veis.
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"A new model for the generation of photoplethysmographic signal with its application to the analysis of beat-to-beat blood pressure variability." 2004. http://library.cuhk.edu.hk/record=b5891881.
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Gu Yingying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (leaves 155-164).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- IPFM Model --- p.1
Chapter 1.1.1 --- Description of IPFM Model --- p.1
Chapter 1.1.2 --- Background of IPFM Related Modeling --- p.3
Chapter 1.2 --- Windkessel Model --- p.8
Chapter 1.2.1 --- Background of the Windkessel Model --- p.8
Chapter 1.2.2 --- Windkessel Related Modeling --- p.13
Chapter 1.3 --- Photoplethysmogram (PPG) --- p.14
Chapter 1.3.1 --- Principle of PPG --- p.14
Chapter 1.3.2 --- Characteristics of PPG Signal --- p.16
Chapter 1.4 --- A Study on the Beat-to-Beat BPV --- p.18
Chapter 1.5 --- Main Purposes of the Study --- p.19
Chapter 1.6 --- Organization of the Thesis --- p.20
Chapter 2 --- Spectral Analysis on the IPFM Process --- p.22
Chapter 2.1 --- Introduction --- p.22
Chapter 2.2 --- A Theoretical Study on the Neural Firing Rate Function --- p.23
Chapter 2.2.1 --- Mathematical Derivation of the Neural Firing Rate --- p.23
Chapter 2.2.2 --- Spectral Analysis of the IPFM Process --- p.27
Chapter 2.2.3 --- Reconstruction of Neural Firing Rate through LPF --- p.30
Chapter 2.3 --- Effects of Neural Dynamics --- p.33
Chapter 2.4 --- Discussion & Conclusion --- p.35
Chapter 3 --- A New Model for the Generation of PPG --- p.37
Chapter 3.1 --- Introduction --- p.37
Chapter 3.2 --- Principles of PPG --- p.38
Chapter 3.2.1 --- Relationship between Pressure and Flow --- p.38
Chapter 3.2.2 --- Peripheral Pressure and Flow Curves --- p.41
Chapter 3.2.3 --- Generation of PPG signal --- p.43
Chapter 3.3 --- Model Description --- p.44
Chapter 3.3.1 --- IPFM model --- p.45
Chapter 3.3.2 --- Windkessel model --- p.46
Chapter 3.3.3 --- New Model for the Generation of PPG --- p.49
Chapter 3.4 --- Simulation --- p.51
Chapter 3.4.1 --- Generation of ECG --- p.51
Chapter 3.4.2 --- Generation of PPG --- p.57
Chapter 3.4.3 --- Effects of the Modulation Depth on the Output --- p.65
Chapter 3.4.4 --- Effects of Mean Autonomic Tone on HRV --- p.72
Chapter 3.5 --- Discussion & Conclusion --- p.75
Chapter 4 --- A Correlation Study on the Beat-to-Beat Features of Photoplethysmographic Signals --- p.80
Chapter 4.1 --- Introduction --- p.80
Chapter 4.2 --- Methodology --- p.81
Chapter 4.2.1 --- Experimental Conditions --- p.81
Chapter 4.2.2 --- Definition of the Parameters --- p.82
Chapter 4.3 --- Data Analysis --- p.85
Chapter 4.3.1 --- At Normal Relaxed State --- p.85
Chapter 4.3.2 --- At Different Levels of Contacting Force --- p.87
Chapter 4.3.3 --- At Different Levels of Local Skin Finger Temperature --- p.90
Chapter 4.3.4 --- At Dynamic State --- p.93
Chapter 4.3.5 --- Repeatability Study --- p.95
Chapter 4.3.6 --- Spectral Analysis --- p.96
Chapter 4.4 --- Discussion --- p.98
Chapter 5 --- The Estimation of the Beat-to-Beat Blood Pressure Variability --- p.103
Chapter 5.1 --- Introduction --- p.103
Chapter 5.2 --- BP Estimation using FY Interval --- p.104
Chapter 5.2.1 --- Multi-Beat BP Estimation under Different Levels of Contacting Force --- p.104
Chapter 5.2.2 --- Beat-to-Beat BP Estimation --- p.108
Chapter 5.2.3 --- Repeatability Study --- p.112
Chapter 5.3 --- A Study on the Beat-to-Beat BPV --- p.113
Chapter 5.3.1 --- Background of the Beat-to-Beat BPV --- p.113
Chapter 5.3.2 --- Analysis of the Beat-to-Beat BPV --- p.115
Chapter 5.4 --- Improving the PPG Model with the Time-Varying BP --- p.120
Chapter 5.4.1 --- Modification of the Model --- p.121
Chapter 5.4.2 --- Simulation --- p.127
Chapter 5.4.3 --- Application of the PPG Model --- p.132
Chapter 5.5 --- Discussion & Conclusion --- p.134
Chapter 6 --- A Novel Biometric Approach --- p.139
Chapter 6.1 --- Introduction --- p.139
Chapter 6.2 --- Human Verification by PPG Signal --- p.140
Chapter 6.2.1 --- Experiment --- p.141
Chapter 6.2.2 --- Feature Extraction --- p.142
Chapter 6.2.3 --- Decision-making --- p.143
Chapter 6.2.4 --- Results --- p.146
Chapter 6.3 --- Discussion --- p.149
Chapter 7 --- Conclusions --- p.151
Chapter 7.1 --- Conclusions of Major Contributions --- p.151
Chapter 7.2 --- Work to Be Done --- p.154
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Гураль, Андрій Богданович, and Andriy Hural. "Метод оцінювання функціонального стану людини для первинного діагностування аритмій." Master's thesis, 2019. http://elartu.tntu.edu.ua/handle/lib/29631.
Full textAbstract:
Проект виконано на кафедрі біотехнічних систем Тернопільського національного технічного університету імені Івана ПулюяДипломну роботу магістра присвячено розробленню методу оцінювання функціонального стану людини для первинного діагностування аритмій. В роботі анотовано фільтрований фотоплетизмографічний сигнал та проведено розмітку піків Р-інтервалів ЕКГ із використанням алгоритму розмітки. Досягнуто покращення якості досліджуваного сигналу шляхом фільтрації РР-інтервалограми, що уможливило підвищення точності результатів аналізу варіабельного серцевого ритму. За результатами побудовано скатерограми для фотоплетизмографічного сигналу із присутністю аритмії та в нормі. Зроблено висновок про те, що різниця між РР-інтервалами вказує на порушення серцевого ритму, тобто присутність аритмії.
The master's thesis is devoted to the development of a method for assessing a person's functional status for the primary diagnosis of arrhythmias. The annotated filtered photoplethysmographic signal was annotated and the ECG P-interval peaks were labeled using a markup algorithm. Improvement of the quality of the investigated signal was achieved by filtering the PP intervals, which made it possible to improve the accuracy of the results of the analysis of the variable heart rate. According to the results, scatograms for the photoplethysmographic signal with the presence of arrhythmia and normal were constructed. It is concluded that the difference between PP-intervals indicates a disturbance of the heart rhythm, ie the presence of arrhythmia.
ЗМІСТ ВСТУП 8 РОЗДІЛ 1. АРТЕРІАЛЬНИЙ ТИСК: СТАНДАРТИ І МЕТОДИ ВИМІРЮВАННЯ, ПОКАЗНИКИ 10 1.1 Вступні завваги 10 1.2 Методи вимірювання артеріального тиску 13 1.3. Визначення АТЛ у біомедичних системах 17 1.4 Вибір шляху досліджень 19 1.5 Висновки до розділу 1 21 РОЗДІЛ 2. МАТЕМАТИЧНА МОДЕЛЬ ДАВАЧА АТЛ 23 2.1 Патентний пошук 23 2.2 Математичне моделювання давача АТЛ 25 2.3 Огляд відомих математичних моделей процесу формування АТЛ 31 2.4 Висновки до розділу 2 34 РОЗДІЛ 3. ПРИСТРІЙ ДЛЯ ВИМІРЮВАННЯ АРТЕРІАЛЬНОГО ТИСКУ 35 3.1 Конструкції давача АТЛ 35 3.2 Конструкція пристрою моніторингу АТЛ 37 3.3. Висновки до розділу 3 40 РОЗДІЛ 4. СПЕЦІАЛЬНА ЧАСТИНА 41 4.1 Методика дослідження електричної активності серця 42 4.2 Обгрунтування вибору прикладного забезпечення для розв’язування наукової задачі 42 4.2 Висновки до розділу 4 46 РОЗДІЛ 5. ОБГРУНТУВАННЯ ЕКОНОМІЧНОЇ ЕФЕКТИВНОСТІ 47 5.1 Науково-технічна актуальність науково-дослідної роботи 47 5.2 Розрахунок витрат на проведення науково-дослідної роботи 48 5.3 Науково-технічна ефективність науково-дослідної роботи 54 5.4 Висновки до розділу 5 58 РОЗДІЛ 6. ОХОРОНА ПРАЦІ ТА БЕЗПЕКА В НАДЗВИЧАЙНИХ СИТУАЦІЯХ 59 6.1 Охорона праці 59 6.2 Безпека в надзвичайних ситуаціях 61 6.3 Висновок до розділу 6 63 РОЗДІЛ 7. ЕКОЛОГІЯ 64 ВИСНОВКИ 69 СПИСОК ВИКОРИСТАНИХ ДЖЕРЕЛ 70 ДОДАТКИ 72
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Schäck, Tim. "Photoplethysmography-Based Biomedical Signal Processing." Phd thesis, 2019. https://tuprints.ulb.tu-darmstadt.de/8411/1/2019-01-24_Sch%C3%A4ck_Tim.pdf.
Full textAbstract:
In this dissertation, photoplethysmography-based biomedical signal processing methods are developed and analyzed. The developed methods solve problems concerning the estimation of the heart rate during physical activity and the monitoring of cardiovascular health. For the estimation of heart rate during physical activity, two methods are presented that are very accurate in estimating the instantaneous heart rate at the wrist and, at the same time, are computationally efficient so that they can easily be integrated into wearables. In the context of cardiovascular health monitoring, a method for the detection of atrial fibrillation using the video camera of a smartphone is proposed that achieves a high detection rate of atrial fibrillation (AF) on a clinical pre-study data set. Further monitoring of cardiovascular parameters includes the estimation of blood pressure (BP), pulse wave velocity (PWV), and vascular age index (VAI), for which an approach is presented that requires only a single photoplethysmographic (PPG) signal.
Heart rate estimation during physical activity using PPG signals constitutes an important research focus of this thesis. In this work, two computationally efficient algorithms are presented that estimate the heart rate from two PPG signals using a three axis accelerometer. In the first approach, adaptive filters are applied to estimate motion artifacts that severely deteriorate the signal quality. The non-stationary relationship between the measured acceleration signals and the artifacts is modeled as a linear system. The outputs of the adaptive filters are combined to further enhance the signal quality and a constrained heart rate tracker follows the most probable high energy continuous line in the spectral domain. The second approach is modest in computational complexity and very fast in execution compared to existing approaches. It combines correlation-based fundamental frequency indicating functions and spectral combination to enhance the correlated useful signal and suppress uncorrelated noise. Additional harmonic noise damping further reduces the impact of strong motion artifacts and a spectral tracking procedure uses a linear least squares prediction. Both approaches are modest in computational complexity and especially the second approach is very fast in execution, as it is shown on a widely used benchmark data set and compared to state-of-the-art methods.
The second research focus and a further major contribution of this thesis lies in the monitoring of the cardiovascular health with a single PPG signal. Two methods are presented, one for detection of AF and one for the estimation of BP, PWV, and VAI. The first method is able to detect AF based on a smartphone filming the finger placed on the video camera. The algorithm transforms the video into a PPG signal and extracts features which are then used to discriminate between AF and normal sinus rhythm (NSR). Perfect detection of AF is already achieved on a data set of 326 measurements (including 20 with AF) that were taken at a clinical pre-study using an appropriate pair of features whereby a decision is formed through a simple linear decision equation. The second method aims at estimating cardiovascular parameters from a single PPG signal without the conventional use of an additional electrocardiogram (ECG). The proposed method extracts a large number of features from the PPG signal and its first and second order difference series, and reconstructs missing features by the use of matrix completion. The estimation of cardiovascular parameters is based on a nonlinear support vector regression (SVR) estimator and compared to single channel PPG based estimators using a linear regression model and a pulse arrival time (PAT) based method. If the training data set contains the person for whom the cardiovascular parameters are to be determined, the proposed method can provide an accurate estimate without further calibration.
All proposed algorithms are applied to real data that we have either recorded ourselves in our biomedical laboratory, that have been recorded by a clinical research partner, or that are freely available as benchmark data sets.
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PLACIDO, CATARINA SOLANGE SUMBO. "Information technology of photoplethysmographic signals analysis." Thesis, 2017. http://elartu.tntu.edu.ua/handle/123456789/19521.
Full textAbstract:
Master’s thesis is devoted to the development of information technology and information system of Photoplethysmographic Signals analysis. In the thesis presented a structure of this information system, that includes different blocks, main of them: block of Registration of photoplethysmographic signal Realizations (BRR), block of statistical Estimation of Diagnostic Parameters (BEDP), block of Diagnostic Space forming (BDS). As a result, the information technology and system was developed. Statistical estimation of covariance function were calculated and presented. New informative factors for FPG-diagnostic - orthogonal decomposition coefficients of the signal mathematical expectation in the basis of discrete exponential functions (DEF) have been identifyed und have been investigated.
IT must therefore be applied as an instrument subject to the goals of medicine, so the main results can be applied in the Decision Making Support Systems in Medicine.
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Ting-YuKuo and 郭庭瑀. "The Analysis of the Photoplethysmography Signal Characteristic." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/75658534438529179755.
Full textAbstract:
碩士國立成功大學
工程科學系
104
This thesis serves to find the characteristics of light sensors for PPG sensing. Though the light sensors are convenient to employ, the quality of the PPG signal is usually affected by noises and motion artifacts. Therefore, it is essential to know the PPG signal features under different conditions. The PPG signal can be obtained by light sensors of different wavelengths; this study uses three kinds of LEDs to implement for examination, including green (525 nm), red (660 nm), infrared (950 nm) lights. The penetration depth varies with respect to wavelengths, the distribution of blood vessels and capillaries is different for each measurement site. Moreover, different skin colors have distinct absorption of light wavelengths. Experiments are designed to examine the relationship between light wavelengths and measurement sites. Heart rate information is deduced by Fourier transform; the frequency of the highest peak indicates the subject’s heart rate. The experimental results show that the infrared light emitting diode provides the strongest signal magnitude among all measurement sites. The subject who has deep color skin causes reduction on the signal magnitude. The changes of the highest peak position is visible before and after physical exercise. Furthermore, the motion artifact can be evidently observed in the frequency domain. The magnitude decreases after exercise for each subject.
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Chen, Yuwei, and 陳昱維. "Study on photoplethysmography signal measurements of respiratory rate." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/68349212714129687672.
Full textAbstract:
碩士聖約翰科技大學
電子工程系碩士班
100
The advantage of Photoplethysmography measurement easy to set up, PPG contains many physiological parameters such as heart rate, respiratory rate, arterial stiffness index, and easy to use and low cost. This study is based on measurement of respiratory rate, the architecture consist of software and hardware, hardware by the high-pass filter, an inverting amplifier, low pass filter combination, is mainly responsible for the PPG of signal acquisition, filtering and signal amplification software by MATLAB finite impulse response filter (FIR) with zero phase shift to the end grab the peak respiratory signal envelope ,this architecture to mention the measurement of respiratory rate in order to facilitate medical staff diagnosis.
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Chen, Ho-Ku, and 陳河谷. "Emotion Recognition Based on Physiological Signals of Photoplethysmographic Signals and Galvanic Skin Response." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/26224468793567763590.
Full textAbstract:
碩士元智大學
通訊工程學系
97
This paper presents a system for emotion recognition using two physiological signals, including photoplethysmographic (PPG) signals and galvanic skin response (GSR). We propose two novel methods for detecting the significant points in photoplethysmographic signals (diastolic trough, systolic peak, dicrotic notch, and dicrotic peak.) Firstly, the method named asymmetric multibandwidth mane-shift extremes seeking provides the ability for detecting maximum and minimum modes in time series signals. Secondly, the method named regression difference bendpoint detection provides a fast and simplified way for locating the dicrotic notch and dicrotic peak. In addition, multiscale entropy analysis is adopted to extract the features from GSR signals. Using fewer physiological signals and significant features with emotional responses are the main ideas in our recognition system. Ten subjects join this experiment and 29 features obtained from the two bio-signals with one person. Support vector machine was used for the classifications. The recognition rate achieved 98%.
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Sun, Hsiang-Yun, and 孫祥耘. "Photoplethysmography Signal Measurements for Cardiopulmonary Performance and Effective Exercise Assessment." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/51803754159684706299.
Full textAbstract:
碩士中原大學
電機工程研究所
99
Abstract In this study, a new physiological parameter ΔPI (Delta Perfusion Index, ΔPI) was proposed based on infrared photoplethysmography as a method of body physiological function measurement, represented the maximum value of the difference changing in peripheral blood perfusion. Meanwhile, a physiological signal measurement system that could be conveniently used at home for heart rate and peripheral tissue blood perfusion PI (Perfusion Index, PI) measurement was designed. By comparing the simple test results of before, in the middle and after exercise, it can perform self-cardiopulmonary function assessment. By the main design of the experiment of the bicycle exercise, each subject's ΔPI was observed in three different default exercise intensities, and followed by the correlation analysis of the results with each subject's physical fitness activity index. The data analysis results of 30 subject's in the average age by 24-year-old showed that after reached effective exercise intensity, subject's ΔPI and the physical fitness activity index correlation was 0.85. When reached aerobic exercise intensity, subject's ΔPI and the physical fitness activity index correlation rose to 0.89. This correlation analysis revealed that the difference value of the maximum amount of peripheral blood perfusion changes (ΔPI) proposed in this study and physical fitness activity index had significant correlation. The results also proved that ΔPI could be useful in assessing the exercise intensity in different levels of physiological indicator scales, which might contribute to the follow-up studies in sports medicine, self-health management and health and other fields. In addition, ECG technology was used as a standard to verify that PPG optical technology also could be applied in heart recovery rate experiment. It was proved by the result of 0.99 (p-value < 0.01) of the correlation between the two.
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Jannah, Raudhatul, and Raudhatul Jannah. "Biometric Personal Identification Based on Photoplethysmography Signal by Heart Rate." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/15711101915723572279.
Full textAbstract:
碩士國立臺灣科技大學
資訊工程系
103
The importance of biometric system can distinguish the uniqueness of personal characteristics. The most popular identification systems have concerned the method based on fingerprint, face detection, iris or hand geometry. This study is trying to improve the biometric system using Photoplethysmography signal by heart rate. The proposed algorithm calculates the contribution of all extracted features to biometric recognition. The efficiency of the proposed algorithms is demonstrated by the experiment results obtained from the Multilayer Perceptron, Naïve Bayes and Random Forest classifier applications based on the extracted features. There are fifty one persons joined for the experiments; the PPG signals of each person were recorded for two different time spans. 30 characteristic features were extracted for each period and these characteristic features are used for the purpose of classification. The results were evaluated via the Multilayer Perceptron, Naïve Bayes and Random Forest classifier models; the true positive rates are then 94.6078 %, 92.1569 % and 90.3922 %, respectively. The obtained results showed that both the proposed algorithm and the biometric identification model based on this developed PPG signal are very promising for contact less recognizing systems.
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Chi, Yen-Ting, and 紀彥廷. "Using Continuous Wavelet Transformation to Develop Dynamic Spectrum of Photoplethysmography Signal and Its Application in Separating Inspiration and Expiration Signals." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/6qr487.
Full textAbstract:
碩士國立中央大學
照明與顯示科技研究所
106
In this thesis, we use Photoplethysmography (PPG) to absorb the light intensity in the cardiovascular circulatory system. The different of light intensity can calculate the relevance of the physiological system In the Algorithm, this paper introduces the continuous wavelet transform (CWT), which can observe the real-time dynamic spectrum analysis, and then we can easily to observe the heart rate variability (HRV), respiratory behavior and the trend of blood in the blood vessels. In this thesis, we prefer to use deep breathing and normal breathing two breathing methods as a template to explore. The depth of the deep breathing and breathing time interval is larger. It can provide a clear and effective indicators; normal breathing is shallow breathing, baseline shift is affect by more autonomic nervous system, and it is difficult to observe the difference in respiratory behavior. In this thesis, the differences of inspiration and expiration are separated by the heart rate variability and restore the real time behavior of inspiration and expiration.
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(10724028), Jason David Ummel. "NONINVASIVE MEASUREMENT OF HEARTRATE, RESPIRATORY RATE, AND BLOOD OXYGENATION THROUGH WEARABLE DEVICES." Thesis, 2021.
Find full textAbstract:
The last two decades have shown a boom in the field of wearable sensing technology. Particularly in the consumer industry, growing trends towards personalized health have pushed new devices to report many vital signs, with a demand for high accuracy and reliability. The most common technique used to gather these vitals is photoplethysmography or PPG. PPG devices are ideal for wearable applications as they are simple, power-efficient, and can be implemented on almost any area of the body. Traditionally PPGs were utilized for capturing just heart rate, however, recent advancements in hardware and digital processing have led to other metrics including respiratory rate (RR) and peripheral oxygen saturation (SpO2), to be reported as well. Our research investigates the potential for wearable devices to be used for outpatient apnea monitoring, and particularly the ability to detect opioid misuse resulting in respiratory depression. Ultimately, the long-term goal of this work is to develop a wearable device that can be used in the rehabilitation process to ensure both accountability and safety of the wearer. This document details contributions towards this goal through the design, development, and evaluation of a device called “Kick Ring”. Primarily, we investigate the ability of Kick Ring to record heartrate (HR), RR, and SpO2. Moreover, we show that the device can calculate RR in real time and can provide an immediate indication of abnormal events such as respiratory depression. Finally, we explore a novel method for reporting apnea events through the use of several PPG characteristics. Kick Ring reliably gathers respiratory metrics and offers a combination of features that does not exist in the current wearables space. These advancements will help to move the field forward, and eventually aid in early detection of life-threatening events.
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Tang-WeiLin and 林唐暐. "Development of a Photoplethysmography-based Forehead-type Physiological Signal Monitoring System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/drtfev.
Full textAbstract:
碩士國立成功大學
資訊工程學系
106
According to the statistics, about 6% population suffers from sleep apnea in the U.S. Sleep apnea may lead to mental discomfort, headache and doze. These symptoms cause discomfort in daily life. Moreover, they may lead to accidents (e.g., car accidents). Therefore, sleep apnea should not be ignored. At present, polysomnography is commonly used to perform overnight sleep recordings so as to determine whether or not the patient has sleep apnea. However, due to the limited resources in hospitals and sleep centers, patients have to wait for weeks or even months for polysomnography. Besides, sleeping in an unfamiliar place such as a hospital or a sleep center may cause poor sleep quality, affecting the accuracy of sleep diagnosis. Generally, polysomnography is not suitable for home use. One reason is that, it is too expensive, and the other reason is that, it requires a long time to setup a larger number of sensors for polysomnography. The proposed system is designed as a wearable device based on photoplethysmography (PPG). The system has low cost, and it does not require setting up a lot of sensors for physiological signal recording. The system can report the degree of blood oxygen saturation, heart rate and respiratory rate. The proposed system has been compared with the polysomnography measurement on oxygen saturation, heart rate and respiratory rate. Besides, by the change of heart rate, the proposed system can detect sleep apnea events. The accuracy of the sleep apnea detection method reaches 84%. Thus, the proposed system can be used for sleep apnea self-screening, allowing the user to evaluate whether or not to go to seek medical attention.
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AN, YONG ZHENG, and 安勇正. "Noninvasive Arteriovenous Fistula Stenosis Evaluation based on Bilateral Photoplethysmography Signals." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/y2z8w3.
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博士南臺科技大學
電機工程系
106
Recently, the number of patients with end-stage renal disease (ESRD) prevalence has significantly increased around the world. Within each age group of ESRD sufferers, the trend is increasing every year. Hemodialysis is the most considered treatment used by a patient with ESRD which filters out unused product from the blood using a device called Dialyzer. In the case of hemodialysis treatment, three vascular access models are well known: Arteriovenous Vistula Fistula, Arteriovenous Venous Graft and Cental Venous Catheter respectively. The Arteriovenous Vistula Fistula (AVF) becomes a vascular access model that is most often recommended by a nephrologist for a hemodialysis patient. There are two general conditions that often occur in the treatment of hemodialysis: vascular access stenosis and impaired vessel wall elasticity. Stenosis is a physiological deformation of blood vessels caused by calcification which leads to narrowing vessel or can be defined as vessel wall thickening caused by new material which lead to abnormal blood pressure. Stenosis causes a decrease in blood flow, so the amount of blood required for the hemodialysis process is reduced. In order to avoid total occlusion caused by this fact, regular monitoring is needed to prolong the life of fistula, otherwise re-making the fistula must be done. Bilateral Photoplethysmography (PPG) is a non-invasive synchronous electro-optic measurement technique for detecting the cardiovascular pulse and physiological information about vascular health at different sites of the body. In this thesis, the degree of stenosis (DOS) of AVF in the recruited subjects was evaluated with the technique called error correcting output coding support vector machine one versus rest (ESVM-OVR). The bilateral differences (asynchronous) of PPG signal were measured and calculated from the left and right fingers in time domain as the features input. The other work represented in this thesis is AVF stenosis classification with Levenberg-Marquardt Neural Network (LM-NN) Algorithm where the slope of bilateral PPG shape were used as the input features. In addition to the techniques used, the proper input features ware investigated in this work to improve the classifier performance. Keywords: hemodialysis, arteriovenous fistula (AVF), stenosis, bilateral photoplethysmography (PPG), degree of stenosis (DOS), ESVM-OVR, LM-NN.
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Chiz-WeiLee and 李知瑋. "A Personal Stress Evaluation and Relaxation System Based on Measurement of Photoplethysmography Signal." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/40196845306797765166.
Full textAbstract:
碩士國立成功大學
電機工程學系碩博士班
100
World Health Organization (WHO) estimates that depression will be the number two cause of lost years of healthy life worldwide by the year 2020. Nowadays, people experience more nervous conditions than ever, including physical and mental aspects, and they would bring out pressure which might be accumulated if we do nothing to release it. Some people are used to relaxing themselves by listening to music or watching videos, and some choose aromatherapy to relieve stress. Till now most of the studies use questionnaires to judge the levels of the users’ relaxation condition, but it’s not objective because the result would be affected by the users’ subjectivities and grading strategies. As to estimating relaxation condition through physiology signal, most studies choose Electrocardiography (ECG), but the method of collecting ECG signals wouldn’t be convenient enough and its signal is electrical signal, which would make some users have misgivings of the safety as operating such devices. Even though there are numbers of ways to relax, but few people could measure easily the level of relaxation and the physiology influence that the various ways result in. This study designs a measure and analysis procedure which is easy to operate and the data could be analyzed rapidly. The system is designed to estimate the users’ regulation conditions of autonomic nervous system (ANS) through analyzing the users’ photoplethysmography (PPG) signals instead of ECG. The study also designs a new therapy of virtual relaxation to study whether different relaxation methods could be integrated or not and if the users’ reception would affect the relaxation results. Study result shows that subjects’ relaxation conditions could be estimated by analyzing their photoplethysmography (PPG) signals, and the average correlation coefficient between LF/HF and STAI questionnaire is 0.82, which represents highly positive relationship. The study integrates relative music, odors, and video to construct visual relaxation therapy, and the experiment results shows that subjects’ LF/HF drops 44%, which is higher than 32%, the result by music therapy. The experiment result also shows highly positive relationship between the subjects’ fondness and the result of the relaxation therapy. The correlation coefficient between fondness and short-term stress relaxation is 0.824, and that between fondness and long-term stress relaxation is 0.807. The study provides a new method to obtain subjects’ regulation conditions of ANS, and it not only improves the inconvenience of traditional ECG measurement, but resolves the subjectivity that questionnaires would bring out. We hope that the study would be helpful for future research about relaxation, thereby, people would be able to have their ANS conditions in hand and choose the most suitable method to relax, and finally develop a physically and mentally healthy life.
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Maulana, Rizal, and 馬慶聖. "Removing Motion Artifact from Heart Rate Signal of Photoplethysmography Using Active Noise Cancellation." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/75408245433086500213.
Full textAbstract:
碩士國立中央大學
電機工程學系
102
This research was aimed at reducing the error caused by motion artifacts in a heart rate detection system to be applied in wearable devices. The heart rate signal was obtained by using the photoplethysmography (PPG) sensor. The PPG sensor we designed consists of a PWM modulator, an infrared LED, a light dependent resistor (LDR), a demodulator, a high-pass filter, a low-pass filter, and an amplification circuit. Furthermore, a 3-axis accelerometer sensor was used to sense the body motion at the PPG sensor site. The output of the accelerometer sensor may have a considerable correlation with the motion artifact in the heart rate signal. Both the PPG heart rate signal and the accelerometer output signal were sampled and digitized through a data acquisition system of a personal computer. The reduction of motion artifact and the heart rate detection were conducted by the MATLAB program in the computer. Taking the accelerometer output signal as the reference signal, an active noise cancellation (ANC) algorithm recovered the corrupted heart rate signal from motion artifact. The performance of the active noise cancellation is evaluated using a commercial heart-rate belt as the golden standard. The heart rate detection error of our system is 3.52% with a small motion, 8.81% with a big motion, and 4.32% with a 1-Hz motion. The result of our experiments proves that the active noise cancellation method is suitable for removing motion artifact from heart rate signal even if there is overlapping between the spectra of the motion artifact and the heart rate signal.
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Wu, Jia-Yang, and 吳家揚. "Non-Invasive Blood Glucose Estimation and Biometric Recognition Method Based on Photoplethysmography Signal." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/mbermw.
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Shen, Chun-Jen, and 沈峻任. "Non-invasive blood glucose monitoring health-care system based on Photoplethysmography(PPG) signal." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/83527451741480718952.
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YungLo and 羅永. "An in-ear motion heart rate monitor based on photoplethysmography and acceleration signals." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/gd659y.
Full textAbstract:
碩士國立成功大學
電機工程學系
102
This thesis presents an in-ear motion heart rate monitoring system based on photoplethysmography (PPG) and acceleration signals. The system is composed of an in-ear headset sensor module and a heart rate monitoring application running on Android devices. The in-ear headset sensor module collects PPG and acceleration signals and transmits them to the heart rate monitoring application via Bluetooth transmission for motion heart rate detection. The heart rate monitoring application executes a motion heart rate detection algorithm that solves the problem that PPG signal is easily corrupted by motion artifacts (MA) when users are in motion. In existing research studies, most researchers used mathematical tools or filters to reduce MA in order to recover PPG signal for heart rate detection. In this paper, the proposed method for heart rate detection from MA corrupted PPG signals is to analyze the power change in the spectra of PPG and acceleration signals. This method can detect heart rate directly without conducting MA cancellation in corrupted PPG signals. Several experiments have been conducted to validate feasibility and effectiveness of the proposed method. The result indicates that the heart rate obtained by the proposed method and Electrocardiogram (ECG) signal are linearly correlated and in a high degree of consistency. Moreover, the error is also within 5% to achieve the minimum requirement of the system applied to heart rate detection of daily activities.
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Khalid, Syed G. "Innovative cuffless blood pressure estimation using photoplethysmography signal only: development and evaluation of machine learning approaches." Thesis, 2019. https://arro.anglia.ac.uk/id/eprint/707374/1/Khalid_2019.pdf.
Full textAbstract:
Blood pressure (BP) is a vital sign commonly monitored for cardiovascular health. To overcome the limitations of inconvenience and discomfort during frequent cuff inflation, cuffless BP measurement has been investigated mainly based on pulse transit time (PTT), which requires frequent calibration and at least two sensors. BP estimation from a single photoplethysmography (PPG) signal using machine learning algorithms has also been attempted, but the AAMI/ISO standard accuracy has not been achieved. Moreover, these published algorithms have not been evaluated under three different BP categories (Hypotensive, Normotensive, and Hypertensive) and three different body postures.
To estimate BP from single PPG signal, three machine learning approaches (Multiple Linear Regression, Support vector machine, Regression Tree) were trained and evaluated on two databases (Queensland and MIMIC-II) which contained PPG and corresponding BPs (SBP and DBP) recordings. Three significant features (total area, rising time, and width 25%) were selected from 17 PPG signal features by multicollinearity test. Two databases were merged into a combined database with less biased BP distribution among three categories, on which three categorically specific BP algorithms were developed from Regression Tree algorithm. Next, a novel two-step approach was developed from K-nearest neighbour (KNN) classification algorithm (to predict BP category) and Regression Tree algorithm (to estimate SBP and DBP). The physiological reliability of the two-step approach was tested on a new database collected from 53 normotensive participants under three different postures (supine, sitting, and stand-up).
Regression Tree algorithm achieved the best overall accuracy on the Queensland and MIMIC databases. In terms of the measurement accuracy for each BP category, the acceptable accuracy (referring to AAMI/ISO) was only achieved in Normotensive subjects from the Queensland database. The three specific BP algorithms achieved acceptable accuracy for each BP category of the combined database. The KNN algorithm achieved a 91.7% BP prediction accuracy. The novel two-step algorithm achieved overall (SBP: -0.07±7.1mmHg, DBP: 0.08±6.0mmHg) and categorical accuracy (Hypotensive, Normotensive and Hypertensive, SBP: -3.0±8.2, -0.7±5.8 and 1.7±7.6mmHg, DBP: -0.3±6.3, -1.8±5.1 and 0.5±5.6mmHg, all p>0.05 when compared with reference BPs) on combined data, and on Normotensive data of different postures (supine, sitting and stand-up, SBP (-2.4±7.2,-1.4±6.3 and -0.68±8.4mmHg), DBP (-1.0±6.3, -2.0±6.6 and 0.2±7.9mmHg). A GUI was finally developed for the laptop and a smartphone app that is connected via WiFi to display the estimation results.
In summary, this study has developed a novel two-step machine learning approach for BP estimation using the PPG signal only. The algorithm has been comprehensively evaluated and achieved ISO acceptable measurement accuracy.
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Chuang, Shang-Yi, and 莊上毅. "A System-on-Chip Design of Ensemble Empirical Mode Decomposition Processor for Photoplethysmography Signals Processing System." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/n2m6p6.
Full textAbstract:
碩士國立交通大學
生醫工程研究所
104
According to statistics regarding the ten leading causes of death among the Taiwanese people, announced by the Department of Health, Executive Yuan, mortalities due to cardiovascular disease (CVDs) accounted for 40.6% of the total number of deaths in 2014. These health concerns may also induce disease and afflictions such as hypertension, stroke, and diabetes. Therefore, innovative health-care systems with high integrativity have become an important topic of research in recent years. This thesis presents Photoplethysmography (PPG) signal processing System-on-Chip (SoC) with an Ensemble Empirical Mode Decomposition (EEMD) processor. This processor has proven to be an adaptive and efficient method for non-linear and non-stationary signal analysis. EEMD decomposes a signal into several narrowband oscillatory components known as the Intrinsic Mode Functions (IMFs) in the time domain. Analyzing each IMF can obtain the physiological information. However, the EEMD processor involves a large number of complicated and iterative computations. It is necessary to use VLSI technology to implement this proposed system with real-time applications. The System-On-Chip design proposed in this thesis is implemented by using Taiwan Semiconductor Manufacturing Company (TSMC) 90 nm tape-out process. To decrease space complexity and speed up timing performance, many innovative and effective modules were developed in this thesis. The proposed hardware architecture only stored one set of white noise in ROM and others could be generated by changing the first reading address. The four stage pipeline architecture is adopted in cubic spline engine to decrease the latency. The result shows that the proposed EEMD hardware architecture can efficiently save 79.4% of the hardware resources and make hardware implementation feasible. Compared with MATLAB software, the proposed EEMD hardware implementation can speed up 23.9 times.
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Wu, Chih-Chin, and 吳智欽. "A Wireless Photoplethysmography Signal Processing System Based on Recursive Least Squares Adaptive Filtering Algorithm for Multiple Physiological Parameters Detection." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/338bkf.
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Chen, I.-Wei, and 陳弈暐. "An Integrated Electrocardiography and Photoplethysmography Signal Processing System Based on Ensemble Empirical Mode Decomposition Method for Multimodal Physiological Data Monitoring." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/yk4fna.
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Liao, Jia-Ju, and 廖家駒. "An Effective Photoplethysmography Signals Processing System Based on Ensemble Empirical Mode Decomposition Method for Acquiring the Multiple Physiological Parameters." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/a5wbqb.
Full textAbstract:
碩士國立交通大學
電子工程學系 電子研究所
104
The heavily medical burden caused by population ageing will become a serious challenge for the current and next generation medical care system. There is an urgent need of low-cost disease prevention and home care programs to lower the possible medical burden in the future. The cardiovascular diseases have been on the list of leading cause of death for years in Taiwan. There is about seventeen million people pass away because of cardiovascular around the world. There is urgent need to get the early prevention tool to reduce the risk of cardiovascular disease all over the world. An effective photoplethysmography (PPG) signal processing system based on ensemble empirical mode decomposition (EEMD) method for acquiring the multiple physiological parameters is proposed in this project. The information of arterial pulse can be obtained by near-infrared. A high quality signal can be extracted through the proposed EEMD algorithm. Based on the most advanced semiconductor industry in Taiwan, the regulation of autonomic nervous system (ANS), RI and SI can be derived in real-time and monitored continuously. It makes the at-home care possible and lowers the rate of cardiovascular diseases and medical expenses through long-term monitoring. PPG signal acquired by the PPG capture circuit is sampled through the ADC at sample frequency of 200Hz after being filtered by the band pass filter. The digitized data are decomposed into IMFs with physiological meanings by the EEMD IC. The output IMFs are wirelessly sent to a computer via a Bluetooth module. Then the regulation of autonomic nervous system , RI and SI can be derived and display on the GUI. To overcome the noise and aliasing effect caused by nonstationary signals, many innovative and effective modules were developed in this thesis. The proposed HHT SoC design could be implemented in hardware with limited resources and fabricated under TSMC 90 nm CMOS technology. To assess the potential risk of cardiovascular, the IMFs with physiological meanings can be extracted from PPG. The RI, SI, LF, HF and VHF can be derived as the parameters to help the diagnosis of cardiovascular disease.
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Yang, Po-Shyang, and 楊博翔. "Analyze of Waveform Signal and Estimate of Blood Pressure Based on Photoplethysmography at the Wrist and the Finger by Using Several Single Wavelength Light-Emitting Diode." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/awucap.
Full textAbstract:
碩士國立中央大學
光電科學與工程學系
107
In recent years, as the proportion of population ageing increases and the proportion of obesity in the population increases, the proportion of cardiovascular disease (CVD) population is increasing and the age group is declining. If there is a more immediate and convenient way to monitor blood pressure, it can effectively prevent cardiovascular diseases. with the rise of wearable devices, monitoring the immediate physiological signals through wearable devices is undoubtedly a way to prevent cardiovascular disease. Therefore, this article will explore the accuracy and comparison of the signals and physiological information obtained from the wearable device. In this thesis, we use reflectance Photoplethysmography (PPG) to explore the physiological signals reflected by the operation of vascular arteries in the human body. Based on the calculation of Bernoulli's principle, the physiological parameter ΔRI (Delta Reflection Index, ΔRI) is used to estimate the blood pressure. Four single-wavelength (530nm, 650nm, 850nm, 940nm) LEDs (Light-Emitting Diode) are used to measure the proximal part of the finger and the radial artery at the wrist. After the waveform stability analysis, the blood pressure estimation experiment was carried out with the LED bulbs of 530 nm and 940 nm as the light source, and compared with the experiment of the fingertip part. As a result of the experiment, the measurement of the 940 nm as the light source at the wrist has a high waveform stability and a low blood pressure measurement error. The SBP error value is 5±0.5% and the DBP error value is 5±1%. In the proximal part of the finger, the waveform stability and blood pressure error value have similar effects under the measurement of 530nm and 940nm. The SBP error range is 7±2% and the DBP error range is 9 ± 2%. Its blood pressure estimation is less reliable.
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Yu, Tzung-Yen, and 余宗諺. "Research in the correlation between photoplethysmography signals and blood flow in vascular access of chronic kidney disease patients for early diagnosis of hemodialysis complications." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/ury668.
Full textAbstract:
碩士國立交通大學
生物科技學系
106
The population of hemodialysis in Taiwan is the highest in the world. With a total number of more than 70,000 people, mainly due to poor medication habits, abuse of all kinds of remedies, population aging and diabetes and other factors, coupled with Taiwan has been a developed nation. Under such pressure, living habits and eating habits will be affect, and even abuse of drugs. In this predictable vicious cycle, the number of hemodialysis patients will only increase but not reduce. This study aims to study the correlation between photoplethysmography signal and blood flow of end- stage of the chronic kidney diseases patients with vascular access whether arteriovenous fistulas or arteriovenous grafts for early diagnosis of hemodialysis complications. Prevention of vascular access complications, such as vascular stenosis and vascular thrombosis, and thus to maintain or improve the quality of life of patients with end-stage chronic kidney diseases, I hope to extend the application to normal people for prevent chronic kidney disease in the future. According to the preliminary clinical results, the blood flow velocity of blood vessels at three wavelengths of the photoplethysmographic sensor has been able to establish a good correlation with the blood flow velocity of blood vessels measured by the standard instrument, and its relevance is significant. In the future we will continue to optimize the algorithm to achieve higher correlation and significance, making the patient in the future to self-home monitoring, improving the quality of life of patients.